Electronic Learning (eLearning) systems are widely used to deliver online learning and education. Increasingly, eLearning systems employ individualization methods to customize the learning experience in an attempt to improve learning outcomes. However, individualization requires significant input from the content provider and/or course author, such as manually tagging content and defining rules for individualization logic that will execute adaptation. Typically course authors need to provide parameters for the transition logic framework, which can be rather cumbersome and time consuming. Additionally, new types of behavioral data collected about students in eLearning courses—including the clicks they make on videos, the time they spend taking assessments, and the text posts that they make on discussion forums—present novel opportunity to define more effective individualization based on performance, behavior, and content, but also runs the risk of making the authoring and teaching processes even more complex.
Hence, it is desirable to design a system that can automate the processes of content tagging and defining individualization decisions based on these tags, using both behavior and performance among the inputs.